Artificial Intelligence (AI) in Health-Care

The term "Artificial Intelligence" is followed by the word "Future." Although, it has already grown to be very popular and started to range its significant impact worldwide. But, what actually is Artificial Intelligence?

Artificial intelligence is the reformation of human intelligence practices by machines, especially computer systems. In general, AI systems work by accumulating excessive amounts of labeled training data. Then, it evaluates the data for correlations and patterns and uses these patterns to predict the outcome. Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Computer Vision, and Cognitive Computing are the sub-domains of AI. These sub-domains are being used differently according to requirements. While science fiction novels and Hollywood movies portray AI as human-like robots that take over the world, the present evolution of AI technologies is not that frightening or quite that smart. Instead, AI has developed to deliver many specific benefits in every industry. For example, AI is far better than human workers at detail-oriented jobs as well as repetitive tasks. It significantly reduces the time for large data tasks and delivers consistent results. AI has already proved its worth by upsurging efficiency in every field it has been tried out. There is no turning back for this ultra digitalised system.

AI has marked its sign in every industry. Healthcare is no different. AI has countless applications in Healthcare. Whether it's getting used to getting relations between genetic codes, maximising hospital efficiency, or powering surgical robots, AI has been a blessing to the healthcare industry. It sorts the lives of patients, doctors, and hospital administrators simpler by executing tasks usually done by human-being but in less time and at a fraction of the value. Here are some significant implications of AI in Healthcare which include - diagnosis and treatment applications, patient engagement and adherence applications, Administrative applications, etc.

·      TO PROFICIENTLY DIAGNOSE AND REDUCE ERROR

Misdiagnosing disease and medical errors accounted for 10% of all US deaths, In 2015. After that, the assurance of improving the diagnostic process is one of AI's most vital healthcare applications. Incomplete medical histories and enormous caseloads can cause deadly human errors. Resistant to those variables, AI can predict and diagnose the disease rapidly than most medical professionals.

·      DIAGNOSING DEADLY BLOOD DISEASES FASTER

AI can be used to diagnose potentially deadly blood diseases at an early stage. Doctors can use AI-advanced microscopes to scan and detect dangerous bacterias (like E. coli and staphylococcus) in blood samples faster than conventional manual scanning. Scientists have already used 25,000 images of blood samples to show the machines how to look for bacteria. The devices then learned the way to identify and predict harmful bacteria in blood with 95% accuracy. Harvard University's teaching hospital, Beth Israel Deaconess Center, has started using AI to diagnose potentially deadly blood diseases early.

·      A SMART SYMPTOM CHECKER

This technology has already been developed by Buoy (Boston, Massachusetts); It is an artificial intelligence-based symptom-cure checker that uses algorithms to track and treat disease. The way it works: a chatbot listens to a patient's symptoms and health concerns, then guides that patient to the proper care supported its diagnosis. Harvard school of medicine is simply one among the various hospitals and healthcare providers that use Buoy's AI to diagnose and treat patients more quickly.

 ·      EARLIER CANCER DETECTION WITH AI

Freenome (San Francisco, California) uses AI in screenings, diagnostic tests, and blood work to check for cancer. Freenome targets distinguish cancer in its earliest stages by installing AI at general screenings and developing new treatments.

·      MORE ACCURATE CANCER DIAGNOSIS

PathAI (Cambridge, Massachusetts); is developing machine learning technology to help pathologists make correct diagnoses. The company's present goals include decreasing errors in cancer diagnosis and developing approaches for individualised medical treatment.

·      AI DEEP LEARNING FOR ACTIONABLE INSIGHTS

Enclitic ( San Francisco, California ); develops deep learning medical tools to streamline radiology diagnoses. This powerful deep learning platform analyses unstructured medical data (radiology blood tests, images, genomics, EKGs, patient medical history) to offer doctors better insight into a patient's real-time needs.

 ·      AI-POWERED RADIOLOGY ASSISTANT

Experts believe Artificial intelligence will enable the next generation of radiology tools that are precise and detailed enough to switch the need for tissue samples in some cases. It can also create more exact images for pathology images.

Zebra ( Shefayim, Israel); Medical Vision provides radiologists with an AI-enabled assistant that receives imaging scans and automatically analyses them for various clinical findings it's studied. The results are passed onto radiologists, who consider the assistant's reports when making a diagnosis.

·      Developing New Medicines with AI IN BIO-PHARMACEUTICAL DEVELOPMENT

The drug development industry is caught up by skyrocketing development costs and research that takes thousands of human hours. It costs about $2.6 billion to place each drug through clinical trials, and only 10% of these drugs are successfully delivered to the marketplace. Thanks to technological breakthroughs, biopharmaceutical companies quickly notice the efficiency, accuracy, and knowledge that AI can provide. For example, in 2007, researchers tasked a robot named Adam with researching the functions of Yeast. Adam scoured billions of knowledge points publicly databases to hypothesise about the parts of 19 genes in Yeast. Also, I predicted nine new and truthful hypotheses. Similarly, another robot Eve, discovered that Triclosan, a standard ingredient in toothpaste, can combat malaria-based parasites. 

BioXcel (New Haven, Connecticut); Therapeutics uses AI to spot and develop new medicines within neuroscience and immuno-oncology. Moreover, the company's drug re-invention program employs AI to seek new claims for existing drugs or spot new patients. From these, we can quickly think of the potentials in developing new medicines holding by AI.

·      TREATING RARE DISEASE WITH AI

AI is taking more space by creating never-before-seen possibilities every year. So, it's not wrong to say that it increases the chances of curing rare diseases. BERG (Framingham, Massachusetts) may be a clinical-stage, AI-based biotech platform that maps conditions to accelerate the invention and progress of revolutionary medicines. By merging its "Interrogative Biology" approach with traditional R&D, BERG can develop more robust product candidates that fight rare diseases. Atomwise (San Francisco, California) uses AI to tackle several of today's most serious conditions, including Ebola and MS. 

·      FINDING BETTER CANDIDATES FOR DEVELOPMENTAL DRUGS

Deep Genomic's (Toronto, Canada) AI platform helps researchers find candidates for developmental drugs associated with neuromuscular and neurodegenerative disorders. Finding the proper candidates during a drug's development has statistically raised the probability of successfully passing clinical trials while decreasing the time and price to plug.

·      DEEP LEARNING FOR TARGETED TREATMENT

The primary goal of BenevolentAI (London, England) is to urge the proper treatment to the appropriate patients in the proper time by using AI to supply a far better target selection and supply previously undiscovered insights through deep learning.

·      STREAMLINING PATIENT EXPERIENCE WITH AI

In the healthcare industry, time is cash. Providing a smooth patient experience allows hospitals, clinics, and physicians to treat more patients nowadays. US hospitals saw quite 35 million patients in 2016, each with different ailments, conditions, and coverage that factor into delivering service. A 2016 report of about 35,000 physicians states that around 96% of patients complain about lack of customer service, negative front desk experiences, a

Inventions in AI healthcare technology reform the patient experience, helping hospital staff process millions, if not billions of knowledge points, faster and more efficiently. We've rounded up six samples of how AI helps Healthcare facilities better manage patient flow. 

·      AUTOMATING HEALTHCARE'S MOST REPETITIVE PROCESSES

By automating most repetitive tasks in a healthcare center or hospital, staff can provide better patient service. And that will automatically positively influence the reputation and finance of the center. Also, it will make the team less tired and more efficient in their respective works. Olive's (Columbus, Ohio) AI platform is meant to automate the healthcare industry's most repetitive tasks, freeing administrators to figure on higher levels. The platform automates everything from un-adjudicated claims to eligibility checks and data migrations, significantly improving healthcare services.

·      REAL-TIME PATIENT FLOW OPTIMIZATION

Qventus (Mountain View, California) is an AI-based software platform that solves operational challenges, including those associated with patient safety and emergency rooms. The company's automated platform prioritises patient complaints, tracks hospital waiting times, and may even chart the fastest ambulance routes.

 ·      INCREASING ACCESS TO HEALTHCARE

AI-enhanced systems can supply personalised and collaborating Healthcare, including face-to-face appointments with doctors anytime. The company's AI-powered chatbot updates the review of a patient's symptoms and recommends either a virtual check-up or a face-to-face visit with a healthcare professional. This practice has been seen in Babylon (New York, New York).

·      USING MACHINE LEARNING (ML) FOR BETTER PATIENT JOURNEY

AI can be used in helping clinics and hospitals manage patient data, payment information, and clinical history by using predictive analytics to intervene at critical junctures within the patient care experience. Healthcare providers can use these insights to move patients through the system with no typical confusion efficiently. CloudMedX (San Francisco, California) uses machine learning to improve patient journeys throughout the healthcare system.

·      PERSONALIZED HEALTHCARE PLANS WITH AI

The Cleveland Clinic (Cleveland, Ohio) teamed up with IBM to infuse its IT capabilities with AI. The world-renowned hospital uses AI to collect information on trillions of administrative and health record data points to streamline the patient experience. This marriage of AI and data helps the Cleveland Clinic personalize healthcare plans on a private basis.

Ethical issues around AI in Healthcare

There are many ethical consequences around the use of AI in Healthcare. The decisions in Healthcare have been made almost exclusively by humans in the past. The assistance of the use of smart machines raises accountability, transparency, permission, and privacy issues.

Perhaps the most challenging issue to address, given today's technologies, is transparency. Many AI-driven deep learning algorithms used for image analysis – are virtually impossible to elucidate. If a patient is informed that an image has led to a cancer diagnosis, they will likely want to know why. Deep learning systems, or even physicians who are generally familiar with their operation, may not explain.

AI systems will undoubtedly make mistakes in patient diagnosis and treatment, and it may be difficult to establish accountability. There are also likely instances in which patients get medical information from AI systems that they would prefer receiving from an empathetic clinician.

We will likely encounter many ethical, medical, occupational, and technological changes with AI in Healthcare. Healthcare institutions and governmental and regulatory bodies must establish structures to monitor critical issues, react responsibly, and establish governance mechanisms to limit negative implications. This is one of the more powerful and consequential technologies to impact human societies, so that it will require continuous attention and thoughtful policy for many years.

 Conclusion

We know that AI has a vital role to play in the healthcare offerings of the future. Even though initial efforts at in-case diagnosis and treatment references have proven thought-provoking, we expect that AI will eventually master that sphere as well. The greatest challenge to AI in these healthcare fields is not whether the technologies will be capable enough to be helpful but rather confirming their adoption in daily clinical practice. For widespread adoption to occur,

It also seems progressively clear that AI systems will not replace human clinicians on a large scale but enhance caring for their patients. Eventually, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion, and big-picture incorporation. Possibly the only healthcare providers who will lose their jobs overtime may be those who refuse to work alongside artificial intelligence.